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Dataset Title:  234Th flux in epipelagic waters at Station ALOHA and the Equatorial Pacific
from R/V Kilo Moana cruises KM1407, KM1418, & KM1515 during 2014-2015
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_806471)
Range: longitude = -158.0 to -155.0°E, latitude = 5.0 to 22.0°N, depth = 5.0 to 200.0m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
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Things You Can Do With Your Graphs

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Cruise {
    String bcodmo_name "cruise_id";
    String description "Cruise identifier";
    String long_name "Cruise";
    String units "unitless";
  Date_initial {
    String bcodmo_name "date";
    String description "Sampling initial date (UTC); format: yyyy-mm-dd";
    String long_name "Date Initial";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "Date_initial";
    String time_precision "1970-01-01";
    String units "unitless";
  Date_final {
    String bcodmo_name "date";
    String description "Sampling final date (UTC); format: yyyy-mm-dd";
    String long_name "Date Final";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String time_precision "1970-01-01";
    String units "unitless";
  Site {
    String bcodmo_name "site";
    String description "Site identifier";
    String long_name "Site";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 5.0, 22.0;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -158.0, -155.0;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  Season {
    String bcodmo_name "season";
    String description "Season";
    String long_name "Season";
    String units "unitless";
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 5.0, 200.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Sampling depth";
    String ioos_category "Location";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  Th_flux {
    String bcodmo_name "thorium-234";
    String description "Integrated 234Th flux; BD = below detection";
    String long_name "Th Flux";
    String units "dpm m-2 d-1";
  Th_flux_error {
    Int16 _FillValue 32767;
    Int16 actual_range 6, 187;
    String bcodmo_name "thorium-234";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Integrated 234Th flux error";
    String long_name "Th Flux Error";
    String units "dpm m-2 d-1";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Total \\u00b2\\u00b3\\u2074Th was measured in samples of seawater collected at
discrete depths using Niskin bottles as described in Umhau et al. (2019).
Briefly, 2 L of seawater was collected at each selected depth. Samples were
spiked with \\u00b2\\u00b3\\u2070Th as a yield tracer and acidified to pH ~ 1
using concentrated nitric acid. After equilibration, ~ 6 hours later,
KMnO\\u2084 and MnCl\\u2082 were added and the pH adjusted to ~ 9 with
concentrated NH\\u2084OH in order to co-precipitate Th via the formation of
MnO\\u2082. After eight hours, total \\u00b2\\u00b3\\u2074Th samples were filtered
onto 25 mm QMA filters, air dried, and frozen.
The \\u00b2\\u00b3\\u2074Th flux (dpm m\\u207b\\u00b2 d\\u207b\\u00b9) was calculated
from the \\u00b2\\u00b3\\u2074Th:\\u00b2\\u00b3\\u2078U disequilibrium and assuming
a steady state (Umhau et al. 2019). The \\u00b2\\u00b3\\u2074Th flux at each
depth was obtained by integrating the flux for the overlying depths using
trapezoidal integration.
Thorium counts were made using a low level RISO beta counter within 2 weeks of
sample collection.";
    String awards_0_award_nid "537122";
    String awards_0_award_number "OCE-1333734";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1333734";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "560590";
    String awards_1_award_number "OCE-1433846";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1433846";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "Donald L. Rice";
    String awards_1_program_manager_nid "51467";
    String cdm_data_type "Other";
    String comment 
"234Th Flux 
  PI: Claudia R. Benitez-Nelson (University of South Carolina) 
  Co-PIs: Brain N. Popp & Jeffrey C. Drazen (Univeristy of Hawaii) 
  Version date: 19-March-2020";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2020-03-19T14:25:49Z";
    String date_modified "2020-03-31T21:12:48Z";
    String defaultDataQuery "&time<now";
    String doi "10.26008/1912/bco-dmo.806471.1";
    Float64 Easternmost_Easting -155.0;
    Float64 geospatial_lat_max 22.0;
    Float64 geospatial_lat_min 5.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -155.0;
    Float64 geospatial_lon_min -158.0;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 200.0;
    Float64 geospatial_vertical_min 5.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-07-19T17:35:16Z (local files)
2024-07-19T17:35:16Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_806471.das";
    String infoUrl "https://www.bco-dmo.org/dataset/806471";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Total 234Th was measured in samples of seawater collected at discrete depths using Niskin bottles.";
    String instruments_0_dataset_instrument_nid "806480";
    String instruments_0_description "A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends.  The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD.  Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_0_supplied_name "Niskin bottles";
    String instruments_1_acronym "Riso Beta Counter";
    String instruments_1_dataset_instrument_description "Thorium counts were made using a low level RISO beta counter within 2 weeks of sample collection.";
    String instruments_1_dataset_instrument_nid "806481";
    String instruments_1_description 
"Low-level beta detectors manufactured by Riso (now Nutech) in Denmark. These instruments accept samples that can be mounted on a 25mm filter holder. These detectors have very low backgrounds, 0.17 counts per minute, and can have counting efficiencies as high as 55%.

    String instruments_1_instrument_name "Riso Laboratory Anti-coincidence Beta Counters";
    String instruments_1_instrument_nid "687";
    String instruments_1_supplied_name "RISO beta counter";
    String keywords "bco, bco-dmo, biological, chemical, cruise, data, dataset, date, Date_final, depth, dmo, erddap, error, final, flux, initial, latitude, longitude, management, oceanography, office, preliminary, season, site, Th_flux, Th_flux_error, time";
    String license "https://www.bco-dmo.org/dataset/806471/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/806471";
    Float64 Northernmost_Northing 22.0;
    String param_mapping "{'806471': {'Latitude': 'flag - latitude', 'Depth': 'flag - depth', 'Longitude': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/806471/parameters";
    String people_0_affiliation "University of South Carolina";
    String people_0_person_name "Claudia R. Benitez-Nelson";
    String people_0_person_nid "51092";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of Hawaii at Manoa";
    String people_1_affiliation_acronym "SOEST";
    String people_1_person_name "Jeffrey C. Drazen";
    String people_1_person_nid "491313";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of Hawaii at Manoa";
    String people_2_affiliation_acronym "SOEST";
    String people_2_person_name "Brian N. Popp";
    String people_2_person_nid "51093";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Shannon Rauch";
    String people_3_person_nid "51498";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "SuspendSinkPart,Hg_Biogeochemistry";
    String projects_0_acronym "SuspendSinkPart";
    String projects_0_description 
"Description from NSF award abstract:
The ocean's midwaters are the largest living space on the planet. The mesopelagic food web plays key roles in the biological carbon pump and the production of food for commercially harvested species, but its functioning is understudied because it is remote and technologically challenging to sample. Recent estimates indicate respiratory demand outstrips measured sinking particle supply by up to 2-3 orders of magnitude suggesting that some food inputs to the mesopelagic food web have been underestimated or missed. Suspended particles frequently are not sampled effectively and may be an overlooked food source. Because identifying the principal inputs of organic matter to the deep-sea food web is critical to understanding its function, the investigators propose to evaluate the relative importance of suspended and sinking particles to the meso- and bathypelagic food web in the central North Pacific. They will characterize the isotopic compositions of specific groups of mesopelagic and bathypelagic zooplankton and micronekton, and identify the extent to which they consume suspended or sinking particles using mass balance approaches. The investigators recently have recognized differences in delta 15N and delta 13C values of amino acids (AA) of sinking and suspended particles; these patterns diverge with depth, providing a means to distinguish between food web pathways. The research will define the source-specific isotopic values of suspended and sinking particles at several depths from the surface to the bathypelagic and test proposed microbial mechanisms driving these depth patterns. At corresponding depths, MOCNESS trawls will sample diverse metazoa: zooplankton size fractions, plus targeted resident, migrating and likely suspension-feeding taxa of zooplankton and micronekton. Preliminary data suggest that suspended particles are a secondary food source, containing less labile organic matter than sinking particles that exhibit a seasonal cycle in flux in the central North Pacific. This study will determine if suspended particles become more important to zooplankton and micronekton during a time of year when sinking particle flux is low (Jan/Feb) in comparison to when it is high (Aug), allowing an evaluation of how temporal change in surface ocean productivity affects the functioning of mesopelagic food webs.
Recent research has called for additional study of the ocean's deep midwaters. This study will provide new insights into the functioning of the meso- and bathypelagic food web and its coupling with surface ocean processes in the central North Pacific. The recently-demonstrated ecological tool of amino acid-specific isotopic analysis will provide a novel and comprehensive approach with which to address our hypotheses, and the project will develop the first AA isotopic dataset spanning particles to fish. Results will help identify the ecological underpinnings of increasing delta 15N values with depth in zooplankton -- apparently a common pattern. Zooplankton consumption of suspended particles also could constitute a mechanistic link between the microbial loop and higher trophic levels. The processes controlling the enormous attenuation of particle flux by mesopelagic consumers -- and thereby the strength of carbon sequestration to the deep ocean -- are not understood. Seasonal sampling will help us relate mesopelagic food web processes to changes in surface ocean productivity, furthering our understanding of future climate change impacts on deep-sea food webs and carbon flux. With regard to fisheries, many oceanic top predators such as tuna and swordfish feed on mesopelagic micronekton. A clearer understanding of the structure of mesopelagic food webs will help inform ecosystem models which are used to understand variation in fisheries production.";
    String projects_0_end_date "2016-12";
    String projects_0_geolocation "Subtropical waters north of Hawaii; Station Aloha (22° 45'N, 158° 00'W)";
    String projects_0_name "Evaluating the relative importance of suspended and sinking particles to the meso and bathypelagic food web in the central North Pacific";
    String projects_0_project_nid "537123";
    String projects_0_start_date "2014-01";
    String projects_1_acronym "Hg_Biogeochemistry";
    String projects_1_description 
"NSF award abstract:
Mercury is a pervasive trace element that exists in several states in the marine environment, including monomethylmercury (MMHg), a neurotoxin that bioaccumulates in marine organisms and poses a human health threat. Understanding the fate of mercury in the ocean and resulting impacts on ocean food webs requires understanding the mechanisms controlling the depths at which mercury chemical transformations occur. Preliminary mercury analyses on nine species of marine fish from the North Pacific Ocean indicated that intermediate waters are an important entry point for MMHg into open ocean food webs. To elucidate the process controlling this, researchers will examine mercury dynamics in regions with differing vertical dissolved oxygen profiles, which should influence depths of mercury transformation. Results of the study will aid in a better understanding of the pathways by which mercury enters the marine food chain and can ultimately impact humans. This project will provide training for graduate and undergraduate students, and spread awareness on oceanic mercury through public outreach and informal science programs.
Mercury isotopic variations can provide insight into a wide variety of environmental processes. Isotopic compositions of mercury display mass-dependent fractionation (MDF) during most biotic and abiotic chemical reactions and mass-independent fractionation (MIF) during photochemical radical pair reactions. The unusual combination of MDF and MIF can provide information on reaction pathways and the biogeochemical history of mercury. Results from preliminary research provide strong evidence that net MMHg formation occurred below the surface mixed layer in the pycnocline and suggested that MMHg in low oxygen intermediate waters is an important entry point for mercury into open ocean food webs. These findings highlight the critical need to understand how MMHg levels in marine biota will respond to changes in atmospheric mercury emissions, deposition of inorganic mercury to the surface ocean, and hypothesized future expansion of oxygen minimum zones. Using field collections across ecosystems with contrasting biogeochemistry and mercury isotope fractionation experiments researchers will fill key knowledge gaps in mercury biogeochemistry. Results of the proposed research will enable scientists to assess the biogeochemical controls on where in the water column mercury methylation and demethylation likely occur.
Related background publication with supplemental data section:
Joel D. Blum, Brian N. Popp, Jeffrey C. Drazen, C. Anela Choy & Marcus W. Johnson. 2013. Methylmercury production below the mixed layer in the North Pacific Ocean. Nature Geoscience 6, 879–884. doi:10.1038/ngeo1918";
    String projects_1_end_date "2017-07";
    String projects_1_geolocation "Pacific Subtropical Gyre, Station ALOHA 22.75N 158W; equatorial Pacific (10N 155W, 5N 155W)";
    String projects_1_name "Collaborative Research: Isotopic insights to mercury in marine food webs and how it varies with ocean biogeochemistry";
    String projects_1_project_nid "560580";
    String projects_1_start_date "2014-08";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 5.0;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "234Th flux in epipelagic waters at Station ALOHA and the Equatorial Pacific from R/V Kilo Moana cruises KM1407, KM1418, & KM1515 during 2014-2015.";
    String title "234Th flux in epipelagic waters at Station ALOHA and the Equatorial Pacific from R/V Kilo Moana cruises KM1407, KM1418, & KM1515 during 2014-2015";
    String version "1";
    Float64 Westernmost_Easting -158.0;
    String xml_source "osprey2erddap.update_xml() v1.3";


Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
For example,
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

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